Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations89505
Missing cells179010
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 MiB
Average record size in memory200.0 B

Variable types

Categorical3
Text2
Unsupported2
Numeric17
DateTime1

Alerts

Crawled_date has constant value "2024-12-05 00:00:00" Constant
Average_Rating is highly overall correlated with Hotel_Name and 6 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading EaseHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Hotel_Name is highly overall correlated with Average_Rating and 8 other fieldsHigh correlation
Num_of_Ratings is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
breadth is highly overall correlated with depth and 1 other fieldsHigh correlation
cleanliness_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
comfort_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
depth is highly overall correlated with breadthHigh correlation
employee_friendliness_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
facility_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
hotel_grade is highly overall correlated with Average_Rating and 4 other fieldsHigh correlation
location_score is highly overall correlated with Hotel_NameHigh correlation
text_length is highly overall correlated with breadthHigh correlation
value_for_money_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
is_photo is highly imbalanced (71.3%) Imbalance
Posted_Date has 89505 (100.0%) missing values Missing
time_lapsed has 89505 (100.0%) missing values Missing
Posted_Date is an unsupported type, check if it needs cleaning or further analysis Unsupported
time_lapsed is an unsupported type, check if it needs cleaning or further analysis Unsupported
Helpfulness has 81272 (90.8%) zeros Zeros
Deviation of star ratings has 2451 (2.7%) zeros Zeros

Reproduction

Analysis started2025-02-05 09:16:33.192033
Analysis finished2025-02-05 09:17:04.503587
Duration31.31 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Hotel_Name
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.4 KiB
thistletower
 
4646
lancaster-gate
 
4637
zedwell-trocaderor
 
4632
stgileshotel
 
4621
z-trafalgar
 
4392
Other values (28)
66577 

Length

Max length35
Median length22
Mean length15.818826
Min length3

Characters and Unicode

Total characters1415864
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstudios2let
2nd rowstudios2let
3rd rowstudios2let
4th rowstudios2let
5th rowstudios2let

Common Values

ValueCountFrequency (%)
thistletower 4646
 
5.2%
lancaster-gate 4637
 
5.2%
zedwell-trocaderor 4632
 
5.2%
stgileshotel 4621
 
5.2%
z-trafalgar 4392
 
4.9%
nyx-hotel-london-by-leonardo-hotels 3567
 
4.0%
radissonblugrafton 3472
 
3.9%
sidneyhotel 3316
 
3.7%
studios2let 3294
 
3.7%
montcalm-chilworth-townhouse 3134
 
3.5%
Other values (23) 49794
55.6%

Length

2025-02-05T18:17:04.565473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
thistletower 4646
 
5.2%
lancaster-gate 4637
 
5.2%
zedwell-trocaderor 4632
 
5.2%
stgileshotel 4621
 
5.2%
z-trafalgar 4392
 
4.9%
nyx-hotel-london-by-leonardo-hotels 3567
 
4.0%
radissonblugrafton 3472
 
3.9%
sidneyhotel 3316
 
3.7%
studios2let 3294
 
3.7%
montcalm-chilworth-townhouse 3134
 
3.5%
Other values (23) 49794
55.6%

Most occurring characters

ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1415864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1415864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1415864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%
Distinct89496
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:04.807001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3585
Median length1848
Mean length206.82469
Min length1

Characters and Unicode

Total characters18511844
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89487 ?
Unique (%)> 99.9%

Sample

1st rowPerfect location with good connections and shops and pubs
2nd rowThe room had everything you needed. Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing
3rd rowConveniently nearby St. Pancras, very small but clean and pleasant room (first floor with small balcony to street side). Interesting area.Luggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests.
4th rowReception staffed 24 hours a day.All good.
5th rowVery convenient to Kings Cross and the cityA little dated could do with a lick of paint
ValueCountFrequency (%)
the 180331
 
5.5%
and 125772
 
3.9%
was 105752
 
3.2%
to 81103
 
2.5%
a 76989
 
2.4%
room 60919
 
1.9%
in 52248
 
1.6%
very 46600
 
1.4%
for 42764
 
1.3%
location 42032
 
1.3%
Other values (73950) 2440724
75.0%
2025-02-05T18:17:05.159415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3170061
17.1%
e 1741276
 
9.4%
o 1349668
 
7.3%
t 1305837
 
7.1%
a 1265869
 
6.8%
n 973710
 
5.3%
r 886563
 
4.8%
i 873286
 
4.7%
s 820340
 
4.4%
l 706295
 
3.8%
Other values (65) 5418939
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18511844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3170061
17.1%
e 1741276
 
9.4%
o 1349668
 
7.3%
t 1305837
 
7.1%
a 1265869
 
6.8%
n 973710
 
5.3%
r 886563
 
4.8%
i 873286
 
4.7%
s 820340
 
4.4%
l 706295
 
3.8%
Other values (65) 5418939
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18511844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3170061
17.1%
e 1741276
 
9.4%
o 1349668
 
7.3%
t 1305837
 
7.1%
a 1265869
 
6.8%
n 973710
 
5.3%
r 886563
 
4.8%
i 873286
 
4.7%
s 820340
 
4.4%
l 706295
 
3.8%
Other values (65) 5418939
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18511844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3170061
17.1%
e 1741276
 
9.4%
o 1349668
 
7.3%
t 1305837
 
7.1%
a 1265869
 
6.8%
n 973710
 
5.3%
r 886563
 
4.8%
i 873286
 
4.7%
s 820340
 
4.4%
l 706295
 
3.8%
Other values (65) 5418939
29.3%

Posted_Date
Unsupported

Missing  Rejected  Unsupported 

Missing89505
Missing (%)100.0%
Memory size699.4 KiB

Rating
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.714235
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:05.245593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9163799
Coefficient of variation (CV)0.24842125
Kurtosis1.9857144
Mean7.714235
Median Absolute Deviation (MAD)1
Skewness-1.2857857
Sum690462.6
Variance3.672512
MonotonicityNot monotonic
2025-02-05T18:17:05.412147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 26129
29.2%
9 16479
18.4%
10 15234
17.0%
7 14990
16.7%
6 6451
 
7.2%
5 3941
 
4.4%
4 2183
 
2.4%
3 1656
 
1.9%
1 1573
 
1.8%
2 799
 
0.9%
Other values (14) 70
 
0.1%
ValueCountFrequency (%)
1 1573
 
1.8%
2 799
 
0.9%
2.5 1
 
< 0.1%
2.9 1
 
< 0.1%
3 1656
1.9%
4 2183
2.4%
4.6 1
 
< 0.1%
5 3941
4.4%
5.4 2
 
< 0.1%
5.8 1
 
< 0.1%
ValueCountFrequency (%)
10 15234
17.0%
9.6 15
 
< 0.1%
9.2 12
 
< 0.1%
9 16479
18.4%
8.8 7
 
< 0.1%
8.3 7
 
< 0.1%
8 26129
29.2%
7.9 7
 
< 0.1%
7.5 5
 
< 0.1%
7.1 3
 
< 0.1%

Average_Rating
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8322563
Minimum7
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:05.483152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.6
median7.7
Q38.1
95-th percentile8.6
Maximum8.7
Range1.7
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.42988557
Coefficient of variation (CV)0.054886556
Kurtosis-0.37402998
Mean7.8322563
Median Absolute Deviation (MAD)0.2
Skewness0.068699345
Sum701026.1
Variance0.1848016
MonotonicityNot monotonic
2025-02-05T18:17:05.560120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.7 21807
24.4%
7.8 8749
9.8%
8.4 7959
 
8.9%
7.9 7510
 
8.4%
7.4 7313
 
8.2%
7 6497
 
7.3%
8.3 5300
 
5.9%
7.6 5222
 
5.8%
8.6 4480
 
5.0%
8 4100
 
4.6%
Other values (5) 10568
11.8%
ValueCountFrequency (%)
7 6497
 
7.3%
7.1 1907
 
2.1%
7.4 7313
 
8.2%
7.5 2065
 
2.3%
7.6 5222
 
5.8%
7.7 21807
24.4%
7.8 8749
9.8%
7.9 7510
 
8.4%
8 4100
 
4.6%
8.1 2409
 
2.7%
ValueCountFrequency (%)
8.7 2423
 
2.7%
8.6 4480
 
5.0%
8.4 7959
 
8.9%
8.3 5300
 
5.9%
8.2 1764
 
2.0%
8.1 2409
 
2.7%
8 4100
 
4.6%
7.9 7510
 
8.4%
7.8 8749
9.8%
7.7 21807
24.4%

Num_of_Ratings
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11821.248
Minimum5613
Maximum39497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:05.643409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5613
5-th percentile5898
Q16664
median9394
Q313923
95-th percentile39497
Maximum39497
Range33884
Interquartile range (IQR)7259

Descriptive statistics

Standard deviation7556.399
Coefficient of variation (CV)0.63922176
Kurtosis6.5265364
Mean11821.248
Median Absolute Deviation (MAD)3059
Skewness2.5057259
Sum1.0580608 × 109
Variance57099167
MonotonicityNot monotonic
2025-02-05T18:17:05.730526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20956 4646
 
5.2%
14445 4637
 
5.2%
39497 4632
 
5.2%
14989 4621
 
5.2%
13923 4392
 
4.9%
9394 3567
 
4.0%
9315 3472
 
3.9%
12641 3316
 
3.7%
11670 3294
 
3.7%
9205 3134
 
3.5%
Other values (23) 49794
55.6%
ValueCountFrequency (%)
5613 1780
2.0%
5715 1836
2.1%
5898 1764
2.0%
5932 2135
2.4%
5933 2256
2.5%
6120 1928
2.2%
6248 1619
1.8%
6277 2297
2.6%
6335 2002
2.2%
6404 1876
2.1%
ValueCountFrequency (%)
39497 4632
5.2%
20956 4646
5.2%
15320 2681
3.0%
14989 4621
5.2%
14445 4637
5.2%
13923 4392
4.9%
12641 3316
3.7%
12340 2795
3.1%
11670 3294
3.7%
11045 2644
3.0%

Helpfulness
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10799397
Minimum0
Maximum14
Zeros81272
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:05.806032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37722244
Coefficient of variation (CV)3.4929955
Kurtosis60.928616
Mean0.10799397
Median Absolute Deviation (MAD)0
Skewness5.3359025
Sum9666
Variance0.14229677
MonotonicityNot monotonic
2025-02-05T18:17:05.876176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 81272
90.8%
1 7145
 
8.0%
2 870
 
1.0%
3 152
 
0.2%
4 36
 
< 0.1%
5 18
 
< 0.1%
6 7
 
< 0.1%
10 2
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 81272
90.8%
1 7145
 
8.0%
2 870
 
1.0%
3 152
 
0.2%
4 36
 
< 0.1%
5 18
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 7
 
< 0.1%
5 18
 
< 0.1%
4 36
 
< 0.1%
3 152
 
0.2%
2 870
 
1.0%
1 7145
8.0%

is_photo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.4 KiB
0
85009 
1
 
4496

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89505
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 85009
95.0%
1 4496
 
5.0%

Length

2025-02-05T18:17:05.955200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T18:17:06.022336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 85009
95.0%
1 4496
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 85009
95.0%
1 4496
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 85009
95.0%
1 4496
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 85009
95.0%
1 4496
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 85009
95.0%
1 4496
 
5.0%
Distinct49587
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:06.239812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length120
Median length105
Mean length29.705257
Min length1

Characters and Unicode

Total characters2658769
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47408 ?
Unique (%)53.0%

Sample

1st rowExceptional
2nd rowVery good
3rd rowConvenient location
4th rowPeaceful position in an elegant street close to 3 major stations and the Bloomsbury area.
5th rowGreat little gem in the city centre
ValueCountFrequency (%)
good 24889
 
5.4%
location 16216
 
3.5%
and 15769
 
3.4%
very 15704
 
3.4%
great 14151
 
3.1%
stay 13440
 
2.9%
a 12645
 
2.8%
for 11823
 
2.6%
the 11114
 
2.4%
hotel 10706
 
2.3%
Other values (10150) 310791
68.0%
2025-02-05T18:17:06.585166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
369129
13.9%
e 248614
 
9.4%
o 234581
 
8.8%
a 197441
 
7.4%
t 191728
 
7.2%
n 149773
 
5.6%
l 128796
 
4.8%
r 128006
 
4.8%
i 122655
 
4.6%
s 92044
 
3.5%
Other values (63) 796002
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2658769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
369129
13.9%
e 248614
 
9.4%
o 234581
 
8.8%
a 197441
 
7.4%
t 191728
 
7.2%
n 149773
 
5.6%
l 128796
 
4.8%
r 128006
 
4.8%
i 122655
 
4.6%
s 92044
 
3.5%
Other values (63) 796002
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2658769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
369129
13.9%
e 248614
 
9.4%
o 234581
 
8.8%
a 197441
 
7.4%
t 191728
 
7.2%
n 149773
 
5.6%
l 128796
 
4.8%
r 128006
 
4.8%
i 122655
 
4.6%
s 92044
 
3.5%
Other values (63) 796002
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2658769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
369129
13.9%
e 248614
 
9.4%
o 234581
 
8.8%
a 197441
 
7.4%
t 191728
 
7.2%
n 149773
 
5.6%
l 128796
 
4.8%
r 128006
 
4.8%
i 122655
 
4.6%
s 92044
 
3.5%
Other values (63) 796002
29.9%

hotel_grade
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.4 KiB
4
39990 
3
36148 
5
6701 
0
4632 
2
 
2034

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89505
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 39990
44.7%
3 36148
40.4%
5 6701
 
7.5%
0 4632
 
5.2%
2 2034
 
2.3%

Length

2025-02-05T18:17:06.670831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T18:17:06.742765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4 39990
44.7%
3 36148
40.4%
5 6701
 
7.5%
0 4632
 
5.2%
2 2034
 
2.3%

Most occurring characters

ValueCountFrequency (%)
4 39990
44.7%
3 36148
40.4%
5 6701
 
7.5%
0 4632
 
5.2%
2 2034
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 39990
44.7%
3 36148
40.4%
5 6701
 
7.5%
0 4632
 
5.2%
2 2034
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 39990
44.7%
3 36148
40.4%
5 6701
 
7.5%
0 4632
 
5.2%
2 2034
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 39990
44.7%
3 36148
40.4%
5 6701
 
7.5%
0 4632
 
5.2%
2 2034
 
2.3%

employee_friendliness_score
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5347634
Minimum7.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:06.815837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile8
Q18.3
median8.6
Q38.7
95-th percentile9.1
Maximum9.1
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.36889506
Coefficient of variation (CV)0.043222647
Kurtosis0.69953941
Mean8.5347634
Median Absolute Deviation (MAD)0.2
Skewness-0.7107023
Sum763904
Variance0.13608357
MonotonicityNot monotonic
2025-02-05T18:17:06.890215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8.7 18186
20.3%
8.6 13683
15.3%
8.1 9253
10.3%
8.4 8871
9.9%
9.1 8651
9.7%
8.5 6722
 
7.5%
9 6027
 
6.7%
8.3 5359
 
6.0%
8.8 4287
 
4.8%
7.5 3783
 
4.2%
Other values (2) 4683
 
5.2%
ValueCountFrequency (%)
7.5 3783
 
4.2%
8 2681
 
3.0%
8.1 9253
10.3%
8.2 2002
 
2.2%
8.3 5359
 
6.0%
8.4 8871
9.9%
8.5 6722
 
7.5%
8.6 13683
15.3%
8.7 18186
20.3%
8.8 4287
 
4.8%
ValueCountFrequency (%)
9.1 8651
9.7%
9 6027
 
6.7%
8.8 4287
 
4.8%
8.7 18186
20.3%
8.6 13683
15.3%
8.5 6722
 
7.5%
8.4 8871
9.9%
8.3 5359
 
6.0%
8.2 2002
 
2.2%
8.1 9253
10.3%

facility_score
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8393822
Minimum6.9
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:06.966304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile6.9
Q17.5
median7.8
Q38.3
95-th percentile8.7
Maximum8.7
Range1.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.50174873
Coefficient of variation (CV)0.064003606
Kurtosis-0.73232438
Mean7.8393822
Median Absolute Deviation (MAD)0.3
Skewness0.074828239
Sum701663.9
Variance0.25175179
MonotonicityNot monotonic
2025-02-05T18:17:07.046424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.8 12069
13.5%
7.5 11924
13.3%
7.6 8815
9.8%
8.7 8634
9.6%
6.9 6497
 
7.3%
8.3 5696
 
6.4%
8 5570
 
6.2%
7.2 4632
 
5.2%
8.4 4392
 
4.9%
7.7 3916
 
4.4%
Other values (7) 17360
19.4%
ValueCountFrequency (%)
6.9 6497
7.3%
7.2 4632
 
5.2%
7.3 1907
 
2.1%
7.4 2681
 
3.0%
7.5 11924
13.3%
7.6 8815
9.8%
7.7 3916
 
4.4%
7.8 12069
13.5%
7.9 2256
 
2.5%
8 5570
6.2%
ValueCountFrequency (%)
8.7 8634
9.6%
8.6 1836
 
2.1%
8.5 2505
 
2.8%
8.4 4392
 
4.9%
8.3 5696
6.4%
8.2 2409
 
2.7%
8.1 3766
 
4.2%
8 5570
6.2%
7.9 2256
 
2.5%
7.8 12069
13.5%

cleanliness_score
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2468521
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:07.123650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.8
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.44250269
Coefficient of variation (CV)0.053657163
Kurtosis-0.33591991
Mean8.2468521
Median Absolute Deviation (MAD)0.3
Skewness-0.17813145
Sum738134.5
Variance0.19580863
MonotonicityNot monotonic
2025-02-05T18:17:07.203505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8.7 11852
13.2%
7.9 9854
11.0%
8.2 9297
10.4%
8.1 9000
10.1%
8.8 8716
9.7%
8 8484
9.5%
8.3 7670
8.6%
8.4 7286
8.1%
7.3 4621
 
5.2%
9.1 4259
 
4.8%
Other values (4) 8466
9.5%
ValueCountFrequency (%)
7.3 4621
5.2%
7.4 1876
 
2.1%
7.5 1907
 
2.1%
7.8 2681
 
3.0%
7.9 9854
11.0%
8 8484
9.5%
8.1 9000
10.1%
8.2 9297
10.4%
8.3 7670
8.6%
8.4 7286
8.1%
ValueCountFrequency (%)
9.1 4259
 
4.8%
8.8 8716
9.7%
8.7 11852
13.2%
8.5 2002
 
2.2%
8.4 7286
8.1%
8.3 7670
8.6%
8.2 9297
10.4%
8.1 9000
10.1%
8 8484
9.5%
7.9 9854
11.0%

comfort_score
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2400223
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:07.282049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.9
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.46634394
Coefficient of variation (CV)0.056594985
Kurtosis-0.52072817
Mean8.2400223
Median Absolute Deviation (MAD)0.3
Skewness-0.13031166
Sum737523.2
Variance0.21747667
MonotonicityNot monotonic
2025-02-05T18:17:07.363535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 12779
14.3%
7.9 9378
10.5%
8.2 8674
9.7%
8.1 8445
9.4%
8.8 8201
9.2%
7.3 6497
7.3%
8.3 6450
7.2%
8.9 6211
6.9%
8.5 5236
 
5.8%
8.7 4392
 
4.9%
Other values (6) 13242
14.8%
ValueCountFrequency (%)
7.3 6497
7.3%
7.4 1907
 
2.1%
7.8 3294
 
3.7%
7.9 9378
10.5%
8 12779
14.3%
8.1 8445
9.4%
8.2 8674
9.7%
8.3 6450
7.2%
8.4 1780
 
2.0%
8.5 5236
5.8%
ValueCountFrequency (%)
9.1 2423
 
2.7%
9 1836
 
2.1%
8.9 6211
6.9%
8.8 8201
9.2%
8.7 4392
4.9%
8.6 2002
 
2.2%
8.5 5236
5.8%
8.4 1780
 
2.0%
8.3 6450
7.2%
8.2 8674
9.7%

value_for_money_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7094911
Minimum7
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:07.440855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.4
median7.7
Q37.9
95-th percentile8.2
Maximum8.3
Range1.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.32641101
Coefficient of variation (CV)0.042338853
Kurtosis-0.65862063
Mean7.7094911
Median Absolute Deviation (MAD)0.2
Skewness-0.16088603
Sum690038
Variance0.10654415
MonotonicityNot monotonic
2025-02-05T18:17:07.518438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.9 20241
22.6%
7.4 13180
14.7%
7.5 10425
11.6%
7.7 8546
9.5%
8.1 7952
 
8.9%
7.6 7760
 
8.7%
7.3 4746
 
5.3%
7 4621
 
5.2%
8.2 4392
 
4.9%
8 4042
 
4.5%
ValueCountFrequency (%)
7 4621
 
5.2%
7.3 4746
 
5.3%
7.4 13180
14.7%
7.5 10425
11.6%
7.6 7760
 
8.7%
7.7 8546
9.5%
7.9 20241
22.6%
8 4042
 
4.5%
8.1 7952
 
8.9%
8.2 4392
 
4.9%
ValueCountFrequency (%)
8.3 3600
 
4.0%
8.2 4392
 
4.9%
8.1 7952
 
8.9%
8 4042
 
4.5%
7.9 20241
22.6%
7.7 8546
9.5%
7.6 7760
 
8.7%
7.5 10425
11.6%
7.4 13180
14.7%
7.3 4746
 
5.3%

location_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1767086
Minimum8.2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:07.586443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.6
Q19
median9.1
Q39.4
95-th percentile9.6
Maximum9.7
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.31118887
Coefficient of variation (CV)0.033910728
Kurtosis0.54790958
Mean9.1767086
Median Absolute Deviation (MAD)0.2
Skewness-0.53207634
Sum821361.3
Variance0.096838514
MonotonicityNot monotonic
2025-02-05T18:17:07.660312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9.1 14059
15.7%
8.9 13828
15.4%
9 11122
12.4%
9.4 10530
11.8%
9.3 9270
10.4%
9.5 7290
8.1%
9.6 7055
7.9%
9.2 5657
6.3%
8.6 4395
 
4.9%
9.7 4392
 
4.9%
ValueCountFrequency (%)
8.2 1907
 
2.1%
8.6 4395
 
4.9%
8.9 13828
15.4%
9 11122
12.4%
9.1 14059
15.7%
9.2 5657
6.3%
9.3 9270
10.4%
9.4 10530
11.8%
9.5 7290
8.1%
9.6 7055
7.9%
ValueCountFrequency (%)
9.7 4392
 
4.9%
9.6 7055
7.9%
9.5 7290
8.1%
9.4 10530
11.8%
9.3 9270
10.4%
9.2 5657
6.3%
9.1 14059
15.7%
9 11122
12.4%
8.9 13828
15.4%
8.6 4395
 
4.9%

Crawled_date
Date

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.4 KiB
Minimum2024-12-05 00:00:00
Maximum2024-12-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-05T18:17:07.729688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:07.792972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

title_length
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1086308
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:07.865557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum29
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.8223059
Coefficient of variation (CV)0.94395271
Kurtosis2.0201067
Mean5.1086308
Median Absolute Deviation (MAD)2
Skewness1.4957671
Sum457248
Variance23.254634
MonotonicityNot monotonic
2025-02-05T18:17:08.037630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 24998
27.9%
2 15310
17.1%
4 6308
 
7.0%
5 6208
 
6.9%
6 5843
 
6.5%
7 4767
 
5.3%
3 4654
 
5.2%
8 3866
 
4.3%
9 3116
 
3.5%
10 2491
 
2.8%
Other values (19) 11944
13.3%
ValueCountFrequency (%)
1 24998
27.9%
2 15310
17.1%
3 4654
 
5.2%
4 6308
 
7.0%
5 6208
 
6.9%
6 5843
 
6.5%
7 4767
 
5.3%
8 3866
 
4.3%
9 3116
 
3.5%
10 2491
 
2.8%
ValueCountFrequency (%)
29 2
 
< 0.1%
28 6
 
< 0.1%
27 19
 
< 0.1%
26 33
 
< 0.1%
25 86
 
0.1%
24 171
0.2%
23 239
0.3%
22 284
0.3%
21 340
0.4%
20 419
0.5%

text_length
Real number (ℝ)

High correlation 

Distinct408
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.369298
Minimum1
Maximum654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:08.127136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median23
Q347
95-th percentile111
Maximum654
Range653
Interquartile range (IQR)36

Descriptive statistics

Standard deviation40.564263
Coefficient of variation (CV)1.1153436
Kurtosis16.986613
Mean36.369298
Median Absolute Deviation (MAD)15
Skewness3.1966573
Sum3255234
Variance1645.4594
MonotonicityNot monotonic
2025-02-05T18:17:08.227242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2601
 
2.9%
5 2562
 
2.9%
9 2531
 
2.8%
7 2530
 
2.8%
8 2495
 
2.8%
10 2375
 
2.7%
4 2341
 
2.6%
11 2254
 
2.5%
12 2248
 
2.5%
14 2061
 
2.3%
Other values (398) 65507
73.2%
ValueCountFrequency (%)
1 603
 
0.7%
2 1263
1.4%
3 1844
2.1%
4 2341
2.6%
5 2562
2.9%
6 2601
2.9%
7 2530
2.8%
8 2495
2.8%
9 2531
2.8%
10 2375
2.7%
ValueCountFrequency (%)
654 1
< 0.1%
568 1
< 0.1%
527 1
< 0.1%
510 1
< 0.1%
503 1
< 0.1%
493 1
< 0.1%
491 1
< 0.1%
470 1
< 0.1%
469 1
< 0.1%
468 1
< 0.1%

time_lapsed
Unsupported

Missing  Rejected  Unsupported 

Missing89505
Missing (%)100.0%
Memory size699.4 KiB

Deviation of star ratings
Real number (ℝ)

Zeros 

Distinct104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3441741
Minimum0
Maximum7.7
Zeros2451
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:08.326391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4
median1
Q31.8
95-th percentile4
Maximum7.7
Range7.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2904159
Coefficient of variation (CV)0.96000655
Kurtosis5.0862224
Mean1.3441741
Median Absolute Deviation (MAD)0.6
Skewness2.0499369
Sum120310.3
Variance1.6651732
MonotonicityNot monotonic
2025-02-05T18:17:08.431813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 7297
 
8.2%
0.4 7184
 
8.0%
0.3 6728
 
7.5%
1.4 4799
 
5.4%
1.3 4293
 
4.8%
0.7 4028
 
4.5%
1 3906
 
4.4%
1.6 3035
 
3.4%
0.1 2979
 
3.3%
2.3 2755
 
3.1%
Other values (94) 42501
47.5%
ValueCountFrequency (%)
0 2451
 
2.7%
0.1 2979
3.3%
0.2 589
 
0.7%
0.2 2535
 
2.8%
0.3 6728
7.5%
0.3 2118
 
2.4%
0.4 7184
8.0%
0.5 894
 
1.0%
0.6 7297
8.2%
0.6 2
 
< 0.1%
ValueCountFrequency (%)
7.7 15
 
< 0.1%
7.6 16
 
< 0.1%
7.4 92
 
0.1%
7.3 52
 
0.1%
7.2 8
 
< 0.1%
7.1 66
 
0.1%
7 86
 
0.1%
6.9 113
 
0.1%
6.8 146
 
0.2%
6.7 432
0.5%

FOG Index
Real number (ℝ)

High correlation 

Distinct1977
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6674118
Minimum0
Maximum142.24
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:08.528979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q16.61
median8.51
Q311.6
95-th percentile18.68
Maximum142.24
Range142.24
Interquartile range (IQR)4.99

Descriptive statistics

Standard deviation5.3495303
Coefficient of variation (CV)0.55335704
Kurtosis18.277331
Mean9.6674118
Median Absolute Deviation (MAD)2.43
Skewness2.6388406
Sum865281.69
Variance28.617475
MonotonicityNot monotonic
2025-02-05T18:17:08.627258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.04 2075
 
2.3%
10 1943
 
2.2%
9.07 1532
 
1.7%
11.6 1457
 
1.6%
8.51 1441
 
1.6%
8.2 1408
 
1.6%
8 1167
 
1.3%
14.53 1009
 
1.1%
13.2 865
 
1.0%
8.13 802
 
0.9%
Other values (1967) 75806
84.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.4 230
 
0.3%
0.8 421
0.5%
1 15
 
< 0.1%
1.08 1
 
< 0.1%
1.2 614
0.7%
1.32 7
 
< 0.1%
1.4 48
 
0.1%
1.48 14
 
< 0.1%
1.52 5
 
< 0.1%
ValueCountFrequency (%)
142.24 1
 
< 0.1%
106.39 1
 
< 0.1%
104.98 1
 
< 0.1%
86.1 1
 
< 0.1%
84.97 1
 
< 0.1%
80.4 4
< 0.1%
74.51 1
 
< 0.1%
72.32 1
 
< 0.1%
66 1
 
< 0.1%
65.48 1
 
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct2213
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.935566
Minimum-555.59
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative2352
Negative (%)2.6%
Memory size699.4 KiB
2025-02-05T18:17:08.725259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-555.59
5-th percentile22.07
Q157.61
median71.82
Q381.29
95-th percentile93.81
Maximum206.84
Range762.43
Interquartile range (IQR)23.68

Descriptive statistics

Standard deviation28.977276
Coefficient of variation (CV)0.43947868
Kurtosis36.546657
Mean65.935566
Median Absolute Deviation (MAD)10.99
Skewness-4.1884291
Sum5901562.8
Variance839.6825
MonotonicityNot monotonic
2025-02-05T18:17:08.826728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.77 761
 
0.9%
73.85 732
 
0.8%
79.26 727
 
0.8%
71.82 715
 
0.8%
81.29 703
 
0.8%
80.28 675
 
0.8%
56.93 671
 
0.7%
64.37 666
 
0.7%
78.25 642
 
0.7%
66.4 637
 
0.7%
Other values (2203) 82576
92.3%
ValueCountFrequency (%)
-555.59 1
 
< 0.1%
-470.99 2
 
< 0.1%
-386.39 4
 
< 0.1%
-301.79 39
 
< 0.1%
-265.85 1
 
< 0.1%
-260.5 1
 
< 0.1%
-219.22 1
 
< 0.1%
-218.2 4
 
< 0.1%
-217.19 98
0.1%
-177.93 1
 
< 0.1%
ValueCountFrequency (%)
206.84 4
 
< 0.1%
121.22 99
0.1%
120.21 146
0.2%
119.19 149
0.2%
118.68 1
 
< 0.1%
118.18 116
0.1%
117.67 4
 
< 0.1%
117.16 102
0.1%
116.86 1
 
< 0.1%
116.65 6
 
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct83186
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54634223
Minimum8.1881423 × 10-18
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:08.929110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8.1881423 × 10-18
5-th percentile0.13008598
Q10.43244792
median0.57926209
Q30.69164551
95-th percentile0.82170573
Maximum1
Range1
Interquartile range (IQR)0.25919759

Descriptive statistics

Standard deviation0.20551205
Coefficient of variation (CV)0.37615992
Kurtosis0.23724848
Mean0.54634223
Median Absolute Deviation (MAD)0.12705512
Skewness-0.64161322
Sum48900.361
Variance0.042235201
MonotonicityNot monotonic
2025-02-05T18:17:09.026866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1130
 
1.3%
0.004922022706 217
 
0.2%
9.745073011 × 10-18124
 
0.1%
2.018798838 × 10-1798
 
0.1%
0.04049735227 93
 
0.1%
0.2797303972 90
 
0.1%
0.1984465399 80
 
0.1%
1.13798216 × 10-1777
 
0.1%
1.026030482 × 10-1777
 
0.1%
9.690374166 × 10-1867
 
0.1%
Other values (83176) 87452
97.7%
ValueCountFrequency (%)
8.188142263 × 10-1831
 
< 0.1%
9.690374166 × 10-1867
0.1%
9.745073011 × 10-18124
0.1%
9.88963349 × 10-181
 
< 0.1%
1.026030482 × 10-1777
0.1%
1.029177741 × 10-171
 
< 0.1%
1.042035196 × 10-1738
 
< 0.1%
1.13798216 × 10-1777
0.1%
1.148738186 × 10-1760
0.1%
1.29645455 × 10-1723
 
< 0.1%
ValueCountFrequency (%)
1 1130
1.3%
0.9715296611 1
 
< 0.1%
0.9618646312 1
 
< 0.1%
0.9610171493 1
 
< 0.1%
0.9609511965 1
 
< 0.1%
0.960657503 1
 
< 0.1%
0.9604759833 1
 
< 0.1%
0.958671232 1
 
< 0.1%
0.9564162594 1
 
< 0.1%
0.9563900494 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct82983
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58197322
Minimum0.032643992
Maximum1.6701841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.4 KiB
2025-02-05T18:17:09.119702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.032643992
5-th percentile0.2625099
Q10.41224664
median0.54627955
Q30.71761788
95-th percentile1.0427207
Maximum1.6701841
Range1.6375401
Interquartile range (IQR)0.30537124

Descriptive statistics

Standard deviation0.24160824
Coefficient of variation (CV)0.41515354
Kurtosis0.92029194
Mean0.58197322
Median Absolute Deviation (MAD)0.14945944
Skewness0.75969015
Sum52089.513
Variance0.058374542
MonotonicityNot monotonic
2025-02-05T18:17:09.219793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03264399233 1130
 
1.3%
1.099357789 217
 
0.2%
1.214987768 124
 
0.1%
1.311139496 115
 
0.1%
1.154836459 107
 
0.1%
1.097742411 93
 
0.1%
0.7213535527 90
 
0.1%
1.248862421 86
 
0.1%
0.7323408524 80
 
0.1%
1.177459644 78
 
0.1%
Other values (82973) 87385
97.6%
ValueCountFrequency (%)
0.03264399233 1130
1.3%
0.08893883701 1
 
< 0.1%
0.09023753393 1
 
< 0.1%
0.09239464589 1
 
< 0.1%
0.100507054 1
 
< 0.1%
0.1009408479 1
 
< 0.1%
0.1025939613 1
 
< 0.1%
0.1029072035 1
 
< 0.1%
0.1036846073 1
 
< 0.1%
0.1063368268 1
 
< 0.1%
ValueCountFrequency (%)
1.670184096 49
0.1%
1.667293504 1
 
< 0.1%
1.664931359 2
 
< 0.1%
1.662843386 1
 
< 0.1%
1.659240055 1
 
< 0.1%
1.656232367 1
 
< 0.1%
1.6539588 1
 
< 0.1%
1.647175179 1
 
< 0.1%
1.645908958 1
 
< 0.1%
1.630870649 1
 
< 0.1%

Interactions

2025-02-05T18:17:02.344990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.060394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.334906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.771271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.225510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.562237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.016017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.378286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.822058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.181367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.639453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.996577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.413789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.751346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.220621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.572805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.039011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.419442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.125893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.486853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.842117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.297535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.633349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.089012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.451495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.894103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.251351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.712896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.067231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.484798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.825960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.294091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.646311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.107955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.501480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.202479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.567382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.926737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.377389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.717631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.167910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.531132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.975244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.332463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.794007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.146404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.564883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.907718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.374515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.728372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.188077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.674223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.278163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.648988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.008079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.458617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.799357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.250988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.613211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.056866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.447927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.878324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.227470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.646632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.992324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.456500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.903040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.266962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.753868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.353581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.728642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.087117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.535548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.880136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.330097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.693320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.136224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.529363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.957736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.305470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.727051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.073398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.534361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.984125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.344927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.836419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.430467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.810325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.169553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.616159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.958666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.411690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.776539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.217437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.609435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.039768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.385392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.806789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.155756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.616990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.068003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.424170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.918979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.508504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.891458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.252479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.697034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.040766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.491722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.856085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.298422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.689107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.120031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.464356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.887578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.327458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.696818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.151330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.502615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.001242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.584208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.972799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.335738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.775910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.121078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.574475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.936659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.381306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.769377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.202774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.631564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.966960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.408515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.779795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.232811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.581912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.083956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.661724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.052253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.416970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.856186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.204363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.654343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.018625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.460660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.849159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.282455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.713093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.048992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.491302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.860733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.316126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.661732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.162221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.735266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.132642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.496466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.933472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.281677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.734605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.096312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.539673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.925072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.362691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.787289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.125114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.571834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.941500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.395969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.736599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.245720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.813670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.212796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.577318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.013093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.364020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.818271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.180277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.620457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.091442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.443309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.869870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.205231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.654754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.021341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.479195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.814604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.325140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.885712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.291616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.655485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.089623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.443105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.896475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.257665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.698564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.168576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.520325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:54.943402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.282451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.734203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.096539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.556877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.888125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.402434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:40.959540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.367466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.733270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.165775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.521404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:47.975305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.423136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.776199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.245729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.598875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.019773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.358853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.813714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.175344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.636052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:01.961781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.487224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.037261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.452392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.817641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.248107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.605582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.059287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.505384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.860808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.327120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.680594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.100532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.440996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.897048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.256749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.719438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.041183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.567895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.114731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.531983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.898520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.326332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.686146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.140500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.585543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:50.940377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.406516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.760370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.180729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.520506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:57.978802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.336432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.800466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.121046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.650918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.189310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.614516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:43.978912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.407037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.859263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.222025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.666790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.023221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.486261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.840648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.258135image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.599344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.061376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.418116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.881289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.198227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:03.726583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:41.260588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:42.688739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:44.055688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:45.480186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:46.934216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:48.297336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:49.741844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:51.097497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:52.561138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:53.915729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:55.333384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:56.673032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:58.138781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:16:59.493446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:00.956022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T18:17:02.268591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-02-05T18:17:09.394057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Average_RatingDeviation of star ratingsFOG IndexFlesch Reading EaseHelpfulnessHotel_NameNum_of_RatingsRatingbreadthcleanliness_scorecomfort_scoredepthemployee_friendliness_scorefacility_scorehotel_gradeis_photolocation_scoretext_lengthtitle_lengthvalue_for_money_score
Average_Rating1.000-0.0050.002-0.039-0.0201.000-0.3230.2810.0090.9190.8830.0100.8440.9410.5040.0670.156-0.0350.0070.709
Deviation of star ratings-0.0051.000-0.0070.010-0.0420.184-0.098-0.041-0.0010.0090.005-0.0530.0230.0230.1240.035-0.1030.044-0.0920.008
FOG Index0.002-0.0071.000-0.749-0.0030.0140.0300.0100.053-0.0030.003-0.0530.0010.0000.0090.0220.034-0.1030.024-0.023
Flesch Reading Ease-0.0390.010-0.7491.0000.0270.023-0.010-0.105-0.126-0.028-0.0340.095-0.040-0.0340.0130.027-0.0260.3010.011-0.002
Helpfulness-0.020-0.042-0.0030.0271.0000.0320.001-0.081-0.077-0.012-0.0090.054-0.036-0.0130.0120.019-0.0180.1190.030-0.006
Hotel_Name1.0000.1840.0140.0230.0321.0001.0000.1530.0341.0001.0000.0461.0001.0001.0000.1341.0000.0220.0351.000
Num_of_Ratings-0.323-0.0980.030-0.0100.0011.0001.000-0.0800.004-0.366-0.2480.009-0.307-0.3350.6060.1110.3750.0030.016-0.374
Rating0.281-0.0410.010-0.105-0.0810.153-0.0801.0000.0810.2650.264-0.0060.2500.2730.1290.0650.066-0.188-0.0060.197
breadth0.009-0.0010.053-0.126-0.0770.0340.0040.0811.0000.0060.004-0.7110.0060.0080.0070.0670.001-0.621-0.153-0.003
cleanliness_score0.9190.009-0.003-0.028-0.0121.000-0.3660.2650.0061.0000.9610.0160.8370.9520.5360.0720.093-0.0230.0060.740
comfort_score0.8830.0050.003-0.034-0.0091.000-0.2480.2640.0040.9611.0000.0240.8100.9430.4830.0610.112-0.0200.0100.646
depth0.010-0.053-0.0530.0950.0540.0460.009-0.006-0.7110.0160.0241.0000.0180.0100.0150.0550.0130.4930.1390.016
employee_friendliness_score0.8440.0230.001-0.040-0.0361.000-0.3070.2500.0060.8370.8100.0181.0000.8160.4250.0900.109-0.0330.0170.683
facility_score0.9410.0230.000-0.034-0.0131.000-0.3350.2730.0080.9520.9430.0100.8161.0000.6530.0690.064-0.030-0.0030.693
hotel_grade0.5040.1240.0090.0130.0121.0000.6060.1290.0070.5360.4830.0150.4250.6531.0000.0610.4260.0150.0100.336
is_photo0.0670.0350.0220.0270.0190.1340.1110.0650.0670.0720.0610.0550.0900.0690.0611.0000.0600.0980.0590.062
location_score0.156-0.1030.034-0.026-0.0181.0000.3750.0660.0010.0930.1120.0130.1090.0640.4260.0601.0000.0050.0580.019
text_length-0.0350.044-0.1030.3010.1190.0220.003-0.188-0.621-0.023-0.0200.493-0.033-0.0300.0150.0980.0051.0000.224-0.015
title_length0.007-0.0920.0240.0110.0300.0350.016-0.006-0.1530.0060.0100.1390.017-0.0030.0100.0590.0580.2241.0000.005
value_for_money_score0.7090.008-0.023-0.002-0.0061.000-0.3740.197-0.0030.7400.6460.0160.6830.6930.3360.0620.019-0.0150.0051.000

Missing values

2025-02-05T18:17:03.869543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-05T18:17:04.204855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_lengthtext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadth
0studios2letPerfect location with good connections and shops and pubsNaN10.07.61167000Exceptional38.37.57.97.87.69.32024-12-0519NaN2.412.4962.340.4165570.843382
1studios2letThe room had everything you needed. Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changingNaN8.07.61167000Very good38.37.57.97.87.69.32024-12-05248NaN0.410.4380.960.5786330.431915
2studios2letConveniently nearby St. Pancras, very small but clean and pleasant room (first floor with small balcony to street side). Interesting area.Luggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests.NaN8.07.61167000Convenient location38.37.57.97.87.69.32024-12-05246NaN0.47.8672.870.5772590.487019
3studios2letReception staffed 24 hours a day.All good.NaN9.07.61167000Peaceful position in an elegant street close to 3 major stations and the Bloomsbury area.38.37.57.97.87.69.32024-12-05157NaN1.48.5181.290.3546810.701785
4studios2letVery convenient to Kings Cross and the cityA little dated could do with a lick of paintNaN8.07.61167000Great little gem in the city centre38.37.57.97.87.69.32024-12-05717NaN0.49.1588.060.6818630.838373
5studios2letLocated in a quiet area but close to Kings Cross station so getting around was easy. Several little pubs nearby for dining and some good coffee shops too.There is no lift so dragging a heavy suitcase up and down stairs was challenging. We had booked a room with terrace but the outdoor space was really minuscule - not what we had expected from the photos.NaN7.07.61167000Convenient, quiet location.38.37.57.97.87.69.32024-12-05365NaN0.68.9072.160.5797460.430384
6studios2letIt's spacious, good value and so very quiet for London.You sometimes have to wriggle the loo flusher to stop it running and runningNaN9.07.61167000Superb38.37.57.97.87.69.32024-12-05123NaN1.44.6076.720.4720330.467403
7studios2letLocationLot of stairs (bad knee)NaN9.07.61167000Ideal location for travelling round38.37.57.97.87.69.32024-12-0555NaN1.410.0066.400.4511540.379700
8studios2letLocation was great, so near the stationWe were on the top floor, six flights of stairs and no lift.\nHeating was on 247 full temperature and no means of reducing it!NaN7.07.61167000Perfect location,38.37.57.97.87.69.32024-12-05231NaN0.611.3681.120.5074670.499533
9studios2letThe location which is excellent for public transport and local dining. \nFriendly staffed reception where we could leave our travel bags all day after checking out.The climb up 3 flights of stairs was exhausting but it was our choice.\nIt was a small room and the kitchen facilities were very sparse ( but we didn't need them)NaN8.07.61167000Ideal accommodation for a short stay in London near St Pancreas station38.37.57.97.87.69.32024-12-051257NaN0.49.1774.190.7762670.430035
Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_lengthtext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadth
89495montanahotelThe hotel was lovely, very clean and tidy. The perfect location for me and my partner as we wanted to stay in Kensington and its very close to Knightsbridge. \nStaff were very friendly super helpful. we did feel a little rushed in the restaurant however we did arriving quite close to closing time. Would most definitely stay again or recommend to family and friends.We didnt have to complain about anything.NaN9.07.8624800Christmas break39.07.78.28.28.09.42024-12-05270NaN1.27.5476.520.5588210.317196
89496montanahotelEverything we needed in the room ,lovely breakfast, excellent location next to tube station and lots of restaurants.The room was too hot.which made it uncomfortable to sleep .Room 509.NaN10.07.8624810Great location39.07.78.28.28.09.42024-12-05229NaN2.28.0270.090.6338150.552477
89497montanahotellocationhot room, shower didnt drain, broken sinkNaN5.07.8624800Great location and generally clean spot but the place is a bit a dated and the basement room was damp, hot and a bit mus39.07.78.28.28.09.42024-12-05257NaN2.88.5138.990.1225240.892616
89498montanahotelGood to have teacoffee and a fridge in the room.The building is beautiful but the interior decor leaves a lot to be desired. The hotel is Indian in style...redgold...faded wallpaper and threadbare carpets...the room was OK but again needed updating. We were in the basement with no view...only rubbish out of the window. There was no hot breakfast so only cereals, fruit and pastries...but it was in a pleasant location...near the tube and shopspubs etc...only a short walk to the Natural History museum.NaN6.07.8624800Lovely building with quite a grand entrance...let down by the interior...fine for overnight stay.39.07.78.28.28.09.42024-12-051483NaN1.86.8663.860.6813690.280244
89499montanahotellocationwater pressure was non existent.\ndespite several request to address the problem.NaN3.07.8624800while the staff was nice. They did very little to remedy the lack of shower and hot water problem we had .39.07.78.28.28.09.42024-12-052212NaN4.89.0723.090.4740540.459680
89500montanahotelConvenient and classy. The staff are excellent people, and Light of India is a fantastic restaurant. I would certainly stay again.NANaN10.07.8624800Highly recommend this little gem situated in my favourite part of town.39.07.78.28.28.09.42024-12-051221NaN2.28.5164.370.6485300.484818
89501montanahotellovely atmosphere, extremely friendly and helpful staff.NaN10.07.8624800Perfect location for our visit to the Royal Albert Hall and the Natural History Museum. would39.07.78.28.28.09.42024-12-05167NaN2.214.2330.530.2121490.927852
89502montanahotelIt was a single room, a little small but it was fine for 1 person, it had everything I neededNaN10.07.8624801The staff were very friendly and helpful. The position was perfect for sightseeing39.07.78.28.28.09.42024-12-051320NaN2.28.0076.560.3258730.795743
89503montanahotelVery clean and well maintained.The rooms are very nice and comfortable with staffs professionalism.The food are delicious,nice breakfast,lunch ,dinner and the cocktails are exceptional.Notting much just that theres no parking.NaN10.07.8624801Myself and my wife really enjoy our stay at this hotel,we love the service and all the staffs are amazing.Looking forwar39.07.78.28.28.09.42024-12-052130NaN2.28.3363.860.5532960.669632
89504montanahotelThe staff were very friendly and helpful! Especially Kampas The hotel was very clean and the fact that they had a wonderful Indian restaurant as part of it was amazing. Best Vindaloo ever!!!!Shower a tad small but adequate xxNaN10.07.8624801Loved every minute! we will be back!! Xxx39.07.78.28.28.09.42024-12-05839NaN2.25.9769.990.5933700.478818